Nearest neighbor classifiers for color image segmentation

We present a class of simple algorithms for color image segmentation based on the nearest neighbor (1-NN) decision rule. The feature vector for each pixel in the image is constructed from color components in HSI space. Since processing all pixels with 1-NN rule is time-consuming, we decided that only some "crate" pixels must be classified with 1-NN, while the others can then be labeled according to their spatial neighborhood containing pixels already classified, and only in relatively rare cases sent to a "global" 1-NN classifier. We test the accuracy and computational efficiency of the algorithms applied to medical image segmentation.